Hearing Womenu2019S Voices : a Study of The Maternity Care Experiences and Needs of Migrant and Refugee Women With Female Genital Mutilation
Bibliographic record
Abstract
Abstract Background: Female genital mutilation (FGM) is a cultural practice defined as the partial or total removal of the external female genitalia for non-therapeutic indications. As a result of changing patterns of migration, clinicians in high-income countries (HICs) such as Australia, Canada, the United Kingdom and the Unites States are caring for women from countries where FGM is traditionally practiced. As many clinicians in these countries are unfamiliar with FGM this poses challenges to the provision of quality of care for these women. Most research focuses on the experiences of providers and the voices of women with FGM are not adequately represented across evidences.Aim: This research, aims to identify best approaches to inform culturally safe and high quality woman centred care and contribute to maternity policy and practice improvements for migrant and refugee women in Australia who have undergone FGM. Methods: We undertook a qualitative study using appreciative inquiry to explore the experience and needs of migrant women with FGM receiving maternity care in Australia. This study aimed to understand the socio-cultural and health needs of these women and opportunities to improve the quality of maternity care for women with FGM in HICs.Results: 23 interviews and four focus groups were conducted with women who had experienced FGM and had a birth in Australia in 2017. The thematic analysis revealed four major categories: as (1) appreciating the best in their experiences, (2) achieving their own dreams, (3) planning together and (4) acting, modifying, improving and sustaining. Conclusion: This study is one of the first of its kind in Australia and provides an understanding of policy, socio-cultural and healthcare gaps, and strategies required to build self-efficacy and improve health outcomes. The recommendations of this research can be used as an advocacy tool or guideline to inform policy and practice and improve the quality of care for affected women through their own voice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".